Privacy-Enhancing k-Anonymization of Customer Data

Summary: Protocols for distributed k‑anonymization: customers keep raw rows; miner only learns a k‑anonymous table—no trusted curator. Two formalizations with provably private, end‑to‑end solutions preventing identifier–sensitive linkage while enabling mining. (summarized by gpt-5-mini on Feb 09 2026)

Paper ID: 1348
Venue: PODS
Year: 2005
Pagerank: 4.714352e-05
Overall Rank: 7,475 | 48.05%
DOI: -

Incoming Non-self Citations Over Time

Authors

Incoming Citations (Sorted by Pagerank)

Showing 1 of 1 citing papers.

Rank	Citing Paper	Year	Venue	Pagerank
2,656	Personalized Privacy Preservation	2006	SIGMOD	8.3636527e-05

Outgoing Citations (Sorted by Pagerank)

Showing 8 of 8 cited papers.

Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.

Rank	Cited Paper	Year	Venue	Pagerank
40	Privacy-Preserving Data Mining	2000	SIGMOD	0.00074213516
84	Statistical Databases: Characteristics, Problems, and Some Solutions	1982	VLDB	0.00053684302
137	Revealing Information while Preserving Privacy	2003	PODS	0.00042381562
148	On the Design and Quantification of Privacy Preserving Data Mining Algorithms	2001	PODS	0.00041196325
177	Limiting Privacy Breaches in Privacy Preserving Data Mining	2003	PODS	0.00037858416
225	Generalizing Data to Provide Anonymity when Disclosing Information	1998	PODS	0.0003266103
305	On the Complexity of Optimal K-Anonymity	2004	PODS	0.00028264843
1,504	Auditing Boolean Attributes	2000	PODS	0.00011607327

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